# 인공 신경망의 층

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## 노트[편집]

### 말뭉치[편집]

- For example, a convolutional layer is usually used in models that are doing work with image data.
^{[1]} - For each node in the second layer, a weighted sum is then computed with each of the incoming connections.
^{[1]} - This process continues until the output layer is reached.
^{[1]} - The number of nodes in the output layer depends on the number of possible output or prediction classes we have.
^{[1]} - Each layer is trying to learn different aspects about the data by minimizing an error/cost function.
^{[2]} - The first layer may learn edge detection, the second may detect eyes, third a nose, etc.
^{[2]} - The output layer is the simplest, usually consisting of a single output for classification problems.
^{[2]} - Although it is a single 'node' it is still considered a layer in a neural network as it could contain multiple nodes.
^{[2]} - A single-layer artificial neural network, also called a single-layer, has a single layer of nodes, as its name suggests.
^{[3]} - Inputs connect directly to the outputs through a single layer of weights.
^{[3]} - A single-layer network can be extended to a multiple-layer network, referred to as a Multilayer Perceptron.
^{[3]} - Input variables, sometimes called the visible layer.
^{[3]} - Think of a layer as a container of neurons.
^{[4]} - There will always be an input and output layer.
^{[4]} - The neurons, within each of the layer of a neural network, perform the same function.
^{[4]} - The input layer is responsible for receiving the inputs.
^{[4]} - Now we continue with the next derivative for the θ parameters between 2nd and 3rd layer.
^{[5]} - Now we got the derivative for θ parameter between 2nd and 3rd layer.
^{[5]} - What we left to do is compute the derivative for θ parameter between input layer and 2nd layer.
^{[5]} - Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer.
^{[6]} - A convolutional neural network consists of an input and an output layer, as well as multiple hidden layers.
^{[6]} - Convolutional layers convolve the input and pass its result to the next layer.
^{[6]} - Pooling layers reduce the dimensions of the data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer.
^{[6]} - backpropagation algorithm enabled practical training of multi-layer networks.
^{[7]} - Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers.
^{[7]} - The layer that receives external data is the input layer.
^{[7]} - The layer that produces the ultimate result is the output layer.
^{[7]} - Central to the convolutional neural network is the convolutional layer that gives the network its name.
^{[8]} - By default, the filters in a convolutional layer are initialized with random weights.
^{[8]} - Let’s take a network trained to classify MNIST handwritten digits, except unlike in the last chapter, we will map directly from the input layer to the output layer with no hidden layers in between.
^{[9]} - Let’s repeat the earlier experiment of observing the weights of a 1-layer neural network with no hidden layer, except this time training on images from CIFAR-10.
^{[9]} - One way we can improve its performance somewhat is by introducing a hidden layer.
^{[9]} - To see, let’s try inserting a middle layer of ten neurons into our MNIST network.
^{[9]} - The following figure shows a perceptron neuron, which is the basic unit of a perceptron layer.
^{[10]} - The activation function of the perceptrons composing each layer determines the function that the neural network represents.
^{[10]} - In the context of neural networks, the scaling function can be thought of as a layer connected to the neural network's inputs.
^{[10]} - The scaling layer contains some basic statistics on the inputs.
^{[10]} - You can think of them as a clustering and classification layer on top of the data you store and manage.
^{[11]} - A node layer is a row of those neuron-like switches that turn on or off as the input is fed through the net.
^{[11]} - Earlier versions of neural networks such as the first perceptrons were shallow, composed of one input and one output layer, and at most one hidden layer in between.
^{[11]} - In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output.
^{[11]} - The image below illustrates how the input values flow into the first layer of neurons.
^{[12]} - The leftmost layer of the network is called the input layer, and the rightmost layer the output layer (which, in this example, has only one node).
^{[13]} - The middle layer of nodes is called the hidden layer, because its values are not observed in the training set.
^{[13]} - We label layer l as L_l , so layer L_1 is the input layer, and layer L_{n_l} the output layer.
^{[13]} - We will write a^{(l)}_i to denote the activation (meaning output value) of unit i in layer l .
^{[13]} - A 3-layer neural network with three inputs, two hidden layers of 4 neurons each and one output layer.
^{[14]} - Notice that when we say N-layer neural network, we do not count the input layer.
^{[14]} - Therefore, a single-layer neural network describes a network with no hidden layers (input directly mapped to output).
^{[14]} - In that sense, you can sometimes hear people say that logistic regression or SVMs are simply a special case of single-layer Neural Networks.
^{[14]} - By following a small set of clear rules, one can programmatically set a competent network architecture (i.e., the number and type of neuronal layers and the number of neurons comprising each layer).
^{[15]} - With respect to the number of neurons comprising this layer, this parameter is completely and uniquely determined once you know the shape of your training data.
^{[15]} - Specifically, the number of neurons comprising that layer is equal to the number of features (columns) in your data.
^{[15]} - So those few rules set the number of layers and size (neurons/layer) for both the input and output layers.
^{[15]} - Next, we find the input for the hidden layer.
^{[16]} - c. Afterward, we have an input for the hidden layer, and it is going to calculate the output by applying a sigmoid function.
^{[16]} - The first matrix shows the output of the hidden layer, which has a size of (4*3).
^{[16]} - e. Afterward, we calculate the output of the output layer by applying a sigmoid function.
^{[16]} - Each hidden layer function is specialized to produce a defined output.
^{[17]} - For example, a hidden layer functions that are used to identify human eyes and ears may be used in conjunction by subsequent layers to identify faces in images.
^{[17]} - We provide a MATLAB computer code for training artificial neural network (ANN) with N+1 layer (N-hidden layer) architecture.
^{[18]} - The N-hidden layer ANN has a general architecture whose sensitivity is the accumulation of the backpropagation of the error between the feedforward output and the target patterns.
^{[18]} - We show that, in both linear and quadratic cases, the learning rate is more flexible for networks with a single hidden layer than for those with multiple hidden layers.
^{[19]} - We also show that single-hidden-layer networks converge faster to linear target functions compared to multiple-hidden-layer networks.
^{[19]} - In the model represented by the following graph, we've added a "hidden layer" of intermediary values.
^{[20]} - In the model represented by the following graph, the value of each node in Hidden Layer 1 is transformed by a nonlinear function before being passed on to the weighted sums of the next layer.
^{[20]} - In brief, each layer is effectively learning a more complex, higher-level function over the raw inputs.
^{[20]} - A set of weights representing the connections between each neural network layer and the layer beneath it.
^{[20]} - However, the most important thing to understand is that a Perceptron with one hidden layer is an extremely powerful computational system.
^{[21]} - Finding the optimal dimensionality for a hidden layer will require trial and error.
^{[21]} - In the same book linked above (on page 159), Dr. Heaton mentions three rules of thumb for choosing the dimensionality of a hidden layer.
^{[21]} - and you believe that the required input–output relationship is fairly straightforward, start with a hidden-layer dimensionality that is equal to two-thirds of the input dimensionality.
^{[21]} - It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.
^{[22]} - Figure 1 shows a one hidden layer MLP with scalar output.
^{[22]} - The output layer receives the values from the last hidden layer and transforms them into output values.
^{[22]} - coefs_ is a list of weight matrices, where weight matrix at index \(i\) represents the weights between layer \(i\) and layer \(i+1\).
^{[22]} - Input Nodes – The Input nodes provide information from the outside world to the network and are together referred to as the “Input Layer”.
^{[23]} - While a feedforward network will only have a single input layer and a single output layer, it can have zero or multiple Hidden Layers.
^{[23]} - A Multi Layer Perceptron has one or more hidden layers.
^{[23]} - A Multi Layer Perceptron (MLP) contains one or more hidden layers (apart from one input and one output layer).
^{[23]} - All dropout does is randomly turn off a percentage of neurons at each layer, at each training step.
^{[24]} - This neural network is formed in three layers, called the input layer, hidden layer, and output layer.
^{[25]} - Each layer consists of one or more nodes, represented in this diagram by the small circles.
^{[25]} - The nodes of the input layer are passive, meaning they do not modify the data.
^{[25]} - Each value from the input layer is duplicated and sent to all of the hidden nodes.
^{[25]} - Our theoretical and experimental results suggest that previously studied model setups that provably give rise to \textit{double descent} might not translate to optimizing two-layer neural networks.
^{[26]} - Each layer in the network is represented by a set of two parameters W matrix (weight matrix) and b matrix (bias matrix).
^{[27]} - For layer, these parameters are represented as and respectively.
^{[27]} - The linear output of layer, i is represented as Zi, and the output after activation is represented as Ai.
^{[27]}

### 소스[편집]

- ↑
^{1.0}^{1.1}^{1.2}^{1.3}Layers in a Neural Network explained - ↑
^{2.0}^{2.1}^{2.2}^{2.3}what is a 'layer' in a neural network - ↑
^{3.0}^{3.1}^{3.2}^{3.3}How to Configure the Number of Layers and Nodes in a Neural Network - ↑
^{4.0}^{4.1}^{4.2}^{4.3}Neural Network Layers - ↑
^{5.0}^{5.1}^{5.2}Everything you need to know about Neural Networks and Backpropagation — Machine Learning Easy and Fun - ↑
^{6.0}^{6.1}^{6.2}^{6.3}Convolutional neural network - ↑
^{7.0}^{7.1}^{7.2}^{7.3}Artificial neural network - ↑
^{8.0}^{8.1}How Do Convolutional Layers Work in Deep Learning Neural Networks? - ↑
^{9.0}^{9.1}^{9.2}^{9.3}Looking inside neural nets - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Neural networks tutorial: Neural network - ↑
^{11.0}^{11.1}^{11.2}^{11.3}A Beginner's Guide to Neural Networks and Deep Learning - ↑ Fully Connected Layers in Convolutional Neural Networks: The Complete Guide
- ↑
^{13.0}^{13.1}^{13.2}^{13.3}Unsupervised Feature Learning and Deep Learning Tutorial - ↑
^{14.0}^{14.1}^{14.2}^{14.3}CS231n Convolutional Neural Networks for Visual Recognition - ↑
^{15.0}^{15.1}^{15.2}^{15.3}How to choose the number of hidden layers and nodes in a feedforward neural network? - ↑
^{16.0}^{16.1}^{16.2}^{16.3}Building Neural Networks with Python Code and Math in Detail — II - ↑
^{17.0}^{17.1}Hidden Layer - ↑
^{18.0}^{18.1}N-hidden layer artificial neural network architecture computer code: geophysical application example - ↑
^{19.0}^{19.1}Comparisons of Single- and Multiple-Hidden-Layer Neural Networks - ↑
^{20.0}^{20.1}^{20.2}^{20.3}Neural Networks: Structure - ↑
^{21.0}^{21.1}^{21.2}^{21.3}How Many Hidden Layers and Hidden Nodes Does a Neural Network Need? - ↑
^{22.0}^{22.1}^{22.2}^{22.3}1.17. Neural network models (supervised) — scikit-learn 0.24.0 documentation - ↑
^{23.0}^{23.1}^{23.2}^{23.3}A Quick Introduction to Neural Networks - ↑ Fundamentals of Neural Networks on Weights & Biases
- ↑
^{25.0}^{25.1}^{25.2}^{25.3}Neural Network Architecture - ↑ Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
- ↑
^{27.0}^{27.1}^{27.2}Deep Neural Network With L